Jocelyn's research interests are in scalable statistical data analysis and computing using tools from randomized algorithms, machine learning, low rank matrix and tensor factorizations, stochastic optimization, and numerical analysis. Her work focuses on developing provable randomized algorithms to scale statistical machine learning methods for the analyses of modern massive, complex, and structured data. Her computational projects have included applications to biobank data, hyperspectral imaging, and survey data containing free-form text responses.
